class_docstr: A base ARIMA class
docstr: Fit an ARIMA to a vector, ``y``, of observations with an
  optional matrix of ``exogenous`` variables, and then generate
  predictions.
  
  Parameters
  ----------
  y : array-like or iterable, shape=(n_samples,)
      The time-series to which to fit the ``ARIMA`` estimator. This may
      either be a Pandas ``Series`` object (statsmodels can internally
      use the dates in the index), or a numpy array. This should be a
      one-dimensional array of floats, and should not contain any
      ``np.nan`` or ``np.inf`` values.
  
  X : array-like, shape=[n_obs, n_vars], optional (default=None)
      An optional 2-d array of exogenous variables. If provided, these
      variables are used as additional features in the regression
      operation. This should not include a constant or trend. Note that
      if an ``ARIMA`` is fit on exogenous features, it must be provided
      exogenous features for making predictions.
  
  n_periods : int, optional (default=10)
      The number of periods in the future to forecast.
  
  fit_args : dict or kwargs, optional (default=None)
      Any keyword args to pass to the fit method.
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code: def fit_predict(self, y, X=None, n_periods=10, **fit_args):
      """Fit an ARIMA to a vector, ``y``, of observations with an
      optional matrix of ``exogenous`` variables, and then generate
      predictions.
  
      Parameters
      ----------
      y : array-like or iterable, shape=(n_samples,)
          The time-series to which to fit the ``ARIMA`` estimator. This may
          either be a Pandas ``Series`` object (statsmodels can internally
          use the dates in the index), or a numpy array. This should be a
          one-dimensional array of floats, and should not contain any
          ``np.nan`` or ``np.inf`` values.
  
      X : array-like, shape=[n_obs, n_vars], optional (default=None)
          An optional 2-d array of exogenous variables. If provided, these
          variables are used as additional features in the regression
          operation. This should not include a constant or trend. Note that
          if an ``ARIMA`` is fit on exogenous features, it must be provided
          exogenous features for making predictions.
  
      n_periods : int, optional (default=10)
          The number of periods in the future to forecast.
  
      fit_args : dict or kwargs, optional (default=None)
          Any keyword args to pass to the fit method.
      """
      self.fit(y, X, **fit_args)
  
      # TODO: remove kwargs from call
      return self.predict(n_periods=n_periods, X=X, **fit_args)
  
file_path: pmdarima/pmdarima/base.py
imports: import abc
  from abc import ABCMeta
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling the BaseARIMA obj's underlying member method code with example usage: obj.fit_predict(y, X, n_periods) and returns: self